Control points deployment in an Intelligent Transportation System for monitoring inter-urban network roadway

Mfenjou, Martin Luther; Ari, Ado Adamou Abba; Njoya, Arouna Ndam; Fotsa-Mbogne, David Jaurès; Kolyang,; Spies, François

doi:10.1016/j.jksuci.2019.10.005

Cited by 9 publications

(12 citation statements)

References 15 publications

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“…As done in [12], the implementation of control points is based on two objectives: to minimize installation costs and to maximize the coverage in the inter-urban road network. The multi objective formulation given as shown in (5) associated with this type of control point according the objectives F 1 and Z 1 as shown in (1) and (2) as well as constraints C 1 and C 2 given in (3) and (4).…”

Section: Our Proposed Scheme 31 Problem Formulation and Design Goalmentioning

confidence: 99%

“…Table 2 presents the notations used in the modeling of the optimization problem given as shown in (12). Concerning the design Figure 2 illustrates the adopted approach to choose one (or more) method(s) according to the evaluation criteria we chose.…”

Section: Our Proposed Scheme 31 Problem Formulation and Design Goalmentioning

confidence: 99%

“…Thus, Mfenjou et al [4] proposed a methodology that aims at contextualizing an ITS by taking into account the infrastructural realities. In [12], the authors proposed a monitoring communication architecture for developing countries' inter-urban road networks based on control points. They then introduced a deployment model for these control points according to the following criteria: minimizing the installation cost of these points, maximizing network coverage and considering areas where accidents are frequently occurring in developing countries' inter-urban road network.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in the context of developing countries, technologies used on the IoT could be exploited for the regulation or monitoring of activities through inter-urban road networks. Likewise, the work of Mfenjou et al [12] focuses on monitoring activities through an inter-urban road network by setting up an architecture of communication based on control points in order to enhance the regulation of inter-urban road networks in developing countries. The proposed deployment was done using an optimization method: NSGA-II.…”

Section: Introductionmentioning

confidence: 99%

“…An adjacency matrix: Consider M the adjacency matrix, M i,j designates the possibility of installing a type i control point, at position j. It corresponds to the variable m as shown in (5) and(12). − A cost matrix of installation of Control Point: This is the cost matrix for deploying control point to admissible positions based on their types.…”

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confidence: 99%

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A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems

Mfenjou

Ari

Njoya

et al. 2021

IJECE

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One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

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Section: Our Proposed Scheme 31 Problem Formulation and Design Goalmentioning

confidence: 99%

Section: Our Proposed Scheme 31 Problem Formulation and Design Goalmentioning

confidence: 99%